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Column | Column title | Variable |
A | FIPS | FIPS code identifying each state |
B | Year | Variable denoting the year and ranges from1994 to 2008 |
C | Fatalities | Fatalities from automobile accidents |
D | DPVM | Fatalities per 100 million vehicle miles driven |
E | SGasTax | State tax on gasoline, $/gallon |
F | RSGasTax | Real state tax on gasoline, 2009$/gallon |
G | CigTax | State tax on cigarettes, dollars per 20-pack |
H | SpTax | State tax on spirits, dollars per gallon |
I | WineTax | State tax on wine, dollars per gallon |
J | BeerTax | State tax on beer, dollars per gallon |
K | RuralInterstateVMD | Vehicle-miles driven in a year on rural interstates, 100 million |
L | RuralTotalVMD | Vehicle-miles driven in a year on all rural roadways, 100 million |
M | UrbanInterstateVMD | Vehicle-miles driven in a year on urban interstates, 100 million |
N | UrbanTotalVMD | Vehicle-miles driven in a year on all urban roadways, 100 million |
O | PU20 | Percent of licensed under the age of 20 |
P | PU25 | Percent of licensed under the age of 25 |
Q | PO70 | Percent of licensed over the age of 70 |
R | PO75 | Percent of licensed over the age of 75 |
S | PO80 | Percent of licensed over the age of 80 |
T | PO85 | Percent of licensed over the age of 85 |
U | BACPS | Dummy variable equal to 1 if the state has adopted the 0.08 BAC per se law; 0 otherwise |
V | RMFI09 | Median family income in a state in 2009 dollars |
Now we are almost ready to present the estimation results from the model. There are a few things we need to cover before we move to presenting the estimation results. First, what, if any, are the econometric issues raised by the model and the data set? In this case we are using a panel data set to estimate the regression:
where fpvmd _{it} is the number of fatalities per 100 million vehicle miles driven in state i in year t, the x _{jit} is the j ^{th} explanatory variable in state i in year t, and ${D}_{it}^{BAC}$ is the dummy variable equal to 1 if state i has a 0.08 per se BAC law in year t . From a policy point of view what we are interested in is the sign of ${\beta}_{k}$ and if ${\beta}_{k}$ is statistically different from zero. At this point it would be appropriate to discuss whether you intend to use a fixed effects or a random effect model. In the interest is simplicity, we will use a fixed effects model but in your own research you would need to consider using either model.
A second issue that needs to be considered is if you plan to use a linear model as specified above or if you might use the natural logarithm of the fatality rate. Since we have no a priori reason to believe that the relationship between the fatality rate and the explanatory variables are linear, we will estimate both log-linear and a log-log models. In this way we can test if our policy conclusions are sensitive to the mathematical specification of our model.
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